UC知道帮忙翻译下。.急要~!!!!急............翻译完在补100分~!
来源：百度知道 编辑：UC知道 时间：2024/06/07 16:07:25
The selection of a subset of input variables is often based on the previous construction of a ranking to order the variables according
to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost
ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is
presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization,
which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is
endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some
state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables.
The experimental validation is made on a
to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost
ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is
presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization,
which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is
endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some
state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables.
The experimental validation is made on a
根据以前建筑达成相关性协议到一个特定标准的可变物,输入变数的一个子集的选择经常命令.然后按照线性查询,估计出包含最上面的子集的质量排列的可变物。根据他们的有用性排列输入变数这一项学习的任务的状况提出构想的算法。这种算法是简单和古典技术的组合的结果，如交互作用和正交化，那些允许明确地也应付多余一种快速的算法的建筑。另外，提出的赋予输入变数的简单的多项扩展应付非线性问题。 与一些的比较科技目前进步水平表明，简单的组分的这个组合能产生输入变数优质。
实验性检验在大范围被做人为数据，避免偏心的估计由于所有特殊学习算法估计是等质量启发使用一个ROC.
呃，不知道。还是请教高人吧
非常想翻译，但没时间了，有时间再来，我先占个位
输入变数的一个子集的选择根据等第的早先建筑经常命令达成协议到相关性
一个特定标准的可变物。 宗旨将然后线性化查寻，估计包含最上面的子集的质量排列的可变物。 根据他们的有用性排列输入变数就一项学习的任务的状况提出构想的算法。 这种算法是简单和古典技术的组合的结果，象交互作用和正交化，哪些允许明确地也应付多余一种快速的算法的建筑。 另外，提出的茂盛赋予输入变数的简单的多项扩展应付非线性问题。 与一些的比较 科技目前进步水平rankers表示，简单的组分的这个组合能产生输入变数优质等第。
实验性检验在大范围被做人为数据集，并且等第的质量使用一个ROC被启发的
设置被估计，避免偏心的估计由于所有特殊学习算法